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<title>Compute Library: MobileNetNetwork&lt; TensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, DepthwiseConvolutionLayerFunction, ReshapeFunction, PoolingLayerFunction &gt; Class Template Reference</title>
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<div class="header">
  <div class="summary">
<a href="#pub-methods">Public Member Functions</a>  </div>
  <div class="headertitle">
<div class="title">MobileNetNetwork&lt; TensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, DepthwiseConvolutionLayerFunction, ReshapeFunction, PoolingLayerFunction &gt; Class Template Reference</div>  </div>
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<p>MobileNet model object.  
 <a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#details">More...</a></p>

<p><code>#include &lt;<a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:ae3ad9a7ab50bae85c5020f337adc7869"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#ae3ad9a7ab50bae85c5020f337adc7869">init</a> (int batches)</td></tr>
<tr class="memdesc:ae3ad9a7ab50bae85c5020f337adc7869"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initialize the network.  <a href="#ae3ad9a7ab50bae85c5020f337adc7869">More...</a><br /></td></tr>
<tr class="separator:ae3ad9a7ab50bae85c5020f337adc7869"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7740c7ab195c03ac140f1f75f633470f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#a7740c7ab195c03ac140f1f75f633470f">build</a> ()</td></tr>
<tr class="memdesc:a7740c7ab195c03ac140f1f75f633470f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Build the model.  <a href="#a7740c7ab195c03ac140f1f75f633470f">More...</a><br /></td></tr>
<tr class="separator:a7740c7ab195c03ac140f1f75f633470f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:acaefe811b78a2fdc4a0dba0c4029c3ef"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">allocate</a> ()</td></tr>
<tr class="memdesc:acaefe811b78a2fdc4a0dba0c4029c3ef"><td class="mdescLeft">&#160;</td><td class="mdescRight">Allocate the network.  <a href="#acaefe811b78a2fdc4a0dba0c4029c3ef">More...</a><br /></td></tr>
<tr class="separator:acaefe811b78a2fdc4a0dba0c4029c3ef"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3b778cda9ac3fad08e7217edbcb942e0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a> ()</td></tr>
<tr class="memdesc:a3b778cda9ac3fad08e7217edbcb942e0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Fills the trainable parameters and input with random data.  <a href="#a3b778cda9ac3fad08e7217edbcb942e0">More...</a><br /></td></tr>
<tr class="separator:a3b778cda9ac3fad08e7217edbcb942e0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3a41262ce9aed70a248ecefae646013b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#a3a41262ce9aed70a248ecefae646013b">feed</a> (std::string name)</td></tr>
<tr class="memdesc:a3a41262ce9aed70a248ecefae646013b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Feed input to network from file.  <a href="#a3a41262ce9aed70a248ecefae646013b">More...</a><br /></td></tr>
<tr class="separator:a3a41262ce9aed70a248ecefae646013b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="memItemLeft" align="right" valign="top">std::vector&lt; unsigned int &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a> ()</td></tr>
<tr class="memdesc:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the classification results.  <a href="#a1466ef70729f3c8b5da5ebfec3f53f26">More...</a><br /></td></tr>
<tr class="separator:a1466ef70729f3c8b5da5ebfec3f53f26"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac8bb3912a3ce86b15842e79d0b421204"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a> ()</td></tr>
<tr class="memdesc:ac8bb3912a3ce86b15842e79d0b421204"><td class="mdescLeft">&#160;</td><td class="mdescRight">Clear all allocated memory from the tensor objects.  <a href="#ac8bb3912a3ce86b15842e79d0b421204">More...</a><br /></td></tr>
<tr class="separator:ac8bb3912a3ce86b15842e79d0b421204"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a13a43e6d814de94978c515cb084873b1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#a13a43e6d814de94978c515cb084873b1">run</a> ()</td></tr>
<tr class="memdesc:a13a43e6d814de94978c515cb084873b1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Runs the model.  <a href="#a13a43e6d814de94978c515cb084873b1">More...</a><br /></td></tr>
<tr class="separator:a13a43e6d814de94978c515cb084873b1"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad55f80ed3cd8b6c4f247763b747016af"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_network.xhtml#ad55f80ed3cd8b6c4f247763b747016af">sync</a> ()</td></tr>
<tr class="memdesc:ad55f80ed3cd8b6c4f247763b747016af"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sync the results.  <a href="#ad55f80ed3cd8b6c4f247763b747016af">More...</a><br /></td></tr>
<tr class="separator:ad55f80ed3cd8b6c4f247763b747016af"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><h3>template&lt;typename TensorType, typename Accessor, typename ActivationLayerFunction, typename ConvolutionLayerFunction, typename DirectConvolutionLayerFunction, typename DepthwiseConvolutionLayerFunction, typename ReshapeFunction, typename PoolingLayerFunction&gt;<br />
class arm_compute::test::networks::MobileNetNetwork&lt; TensorType, Accessor, ActivationLayerFunction, ConvolutionLayerFunction, DirectConvolutionLayerFunction, DepthwiseConvolutionLayerFunction, ReshapeFunction, PoolingLayerFunction &gt;</h3>

<p>MobileNet model object. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00053">53</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>
</div><h2 class="groupheader">Member Function Documentation</h2>
<a class="anchor" id="acaefe811b78a2fdc4a0dba0c4029c3ef"></a>
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<div class="memproto">
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  <td class="mlabels-left">
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          <td class="memname">void allocate </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td></td>
        </tr>
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  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
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<p>Allocate the network. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00113">113</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>
<div class="fragment"><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    {</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        input.allocator()-&gt;allocate();</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;        output.allocator()-&gt;allocate();</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;        w_conv3x3.allocator()-&gt;allocate();</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        b_conv3x3.allocator()-&gt;allocate();</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_conv.size(); ++i)</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;        {</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;            w_conv[i].allocator()-&gt;allocate();</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;            b_conv[i].allocator()-&gt;allocate();</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        }</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_dwc.size(); ++i)</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;        {</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;            w_dwc[i].allocator()-&gt;allocate();</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;            b_dwc[i].allocator()-&gt;allocate();</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        }</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;o : conv_out)</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;        {</div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;            o.allocator()-&gt;allocate();</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;        }</div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;o : dwc_out)</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;        {</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;            o.allocator()-&gt;allocate();</div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;        }</div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;        pool_out.allocator()-&gt;allocate();</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    }</div></div><!-- fragment -->
</div>
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          <td class="memname">void build </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
          <td></td>
        </tr>
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  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span>  </td>
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<p>Build the model. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00088">88</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::AVG</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::BOUNDED_RELU</a>, <a class="el" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">arm_compute::FLOOR</a>, and <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::LOGISTIC</a>.</p>
<div class="fragment"><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    {</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        <span class="comment">// Configure Layers</span></div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        conv3x3.configure(&amp;input, &amp;w_conv3x3, &amp;b_conv3x3, &amp;conv_out[0], <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>));</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        conv3x3_act.configure(&amp;conv_out[0], <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f));</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        depthwise_conv_block_build(0, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        depthwise_conv_block_build(1, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        depthwise_conv_block_build(2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        depthwise_conv_block_build(3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        depthwise_conv_block_build(4, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;        depthwise_conv_block_build(5, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        depthwise_conv_block_build(6, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        depthwise_conv_block_build(7, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        depthwise_conv_block_build(8, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        depthwise_conv_block_build(9, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        depthwise_conv_block_build(10, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        depthwise_conv_block_build(11, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        depthwise_conv_block_build(12, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        pool.configure(&amp;conv_out[13], &amp;pool_out, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>, 7, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)));</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        conv1x1[13].configure(&amp;pool_out, &amp;w_conv[13], &amp;b_conv[13], &amp;conv_out[14], <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        logistic.configure(&amp;conv_out[14], <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>));</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        reshape.configure(&amp;conv_out[14], &amp;output);</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    }</div><div class="ttc" id="namespacearm__compute_xhtml_a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">arm_compute::DimensionRoundingType::FLOOR</a></div><div class="ttdoc">Floor rounding. </div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00809">Types.h:809</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00571">Types.h:571</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic (  ) </div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier (  ) </div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a"><div class="ttname"><a href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::PoolingType::AVG</a></div><div class="ttdoc">Average Pooling. </div></div>
<div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00688">Types.h:688</a></div></div>
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          <td class="memname">void clear </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
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<p>Clear all allocated memory from the tensor objects. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00207">207</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>
<div class="fragment"><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    {</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;        input.allocator()-&gt;free();</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        output.allocator()-&gt;free();</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        w_conv3x3.allocator()-&gt;free();</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;        b_conv3x3.allocator()-&gt;free();</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_conv.size(); ++i)</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        {</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;            w_conv[i].allocator()-&gt;free();</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;            b_conv[i].allocator()-&gt;free();</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;        }</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_dwc.size(); ++i)</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;        {</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;            w_dwc[i].allocator()-&gt;free();</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;            b_dwc[i].allocator()-&gt;free();</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        }</div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;o : conv_out)</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        {</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;            o.allocator()-&gt;free();</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;        }</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;        <span class="keywordflow">for</span>(<span class="keyword">auto</span> &amp;o : dwc_out)</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        {</div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;            o.allocator()-&gt;free();</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        }</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        pool_out.allocator()-&gt;free();</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    }</div></div><!-- fragment -->
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          <td class="memname">void feed </td>
          <td>(</td>
          <td class="paramtype">std::string&#160;</td>
          <td class="paramname"><em>name</em></td><td>)</td>
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<p>Feed input to network from file. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">name</td><td>File name of containing the input data. </td></tr>
  </table>
  </dd>
</dl>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00166">166</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="main_8cpp_source.xhtml#l00059">arm_compute::test::library</a>.</p>
<div class="fragment"><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;    {</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill_layer_data(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(input), name);</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;    }</div><div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml">arm_compute::test::Accessor</a></div><div class="ttdoc">Accessor implementation for Tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00035">Accessor.h:35</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a71326f0909d77386e29b511e1990a11f"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr&lt; AssetsLibrary &gt; library</div><div class="ttdef"><b>Definition:</b> <a href="main_8cpp_source.xhtml#l00059">main.cpp:59</a></div></div>
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          <td class="memname">void fill_random </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
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<p>Fills the trainable parameters and input with random data. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00142">142</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="main_8cpp_source.xhtml#l00059">arm_compute::test::library</a>.</p>
<div class="fragment"><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;    {</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>                     seed_idx = 0;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;        std::uniform_real_distribution&lt;&gt; distribution(-1, 1);</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(input), distribution, seed_idx++);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(w_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;        <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(b_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_conv.size(); ++i)</div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;        {</div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(w_conv[i]), distribution, seed_idx++);</div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(b_conv[i]), distribution, seed_idx++);</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;        }</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; w_dwc.size(); ++i)</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        {</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(w_dwc[i]), distribution, seed_idx++);</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;            <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>-&gt;fill(<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>(b_dwc[i]), distribution, seed_idx++);</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        }</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;    }</div><div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml">arm_compute::test::Accessor</a></div><div class="ttdoc">Accessor implementation for Tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00035">Accessor.h:35</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_xhtml_a71326f0909d77386e29b511e1990a11f"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr&lt; AssetsLibrary &gt; library</div><div class="ttdef"><b>Definition:</b> <a href="main_8cpp_source.xhtml#l00059">main.cpp:59</a></div></div>
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          <td class="memname">std::vector&lt;unsigned int&gt; get_classifications </td>
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<p>Get the classification results. </p>
<dl class="section return"><dt>Returns</dt><dd><a class="el" href="struct_vector.xhtml" title="Structure to hold Vector information. ">Vector</a> containing the classified labels </dd></dl>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00175">175</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="_window_8h_source.xhtml#l00043">Window::DimX</a>, <a class="el" href="_helpers_8inl_source.xhtml#l00122">arm_compute::execute_window_loop()</a>, <a class="el" href="_dimensions_8h_source.xhtml#l00122">Dimensions&lt; T &gt;::num_dimensions()</a>, <a class="el" href="_window_8inl_source.xhtml#l00041">Window::set()</a>, <a class="el" href="_accessor_8h_source.xhtml#l00087">Accessor::shape()</a>, and <a class="el" href="_dimensions_8h_source.xhtml#l00081">Dimensions&lt; T &gt;::x()</a>.</p>
<div class="fragment"><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;    {</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;        std::vector&lt;unsigned int&gt; classified_labels;</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;        <a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>                  output_accessor(output);</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 1, 1));</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 1; d &lt; output_accessor.shape().num_dimensions(); ++d)</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;        {</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;            window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, output_accessor.shape()[d], 1));</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        }</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&amp;](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> &amp; <span class="keywordtype">id</span>)</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        {</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;            <span class="keywordtype">int</span>               max_idx = 0;</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;            <span class="keywordtype">float</span>             val     = 0;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;            <span class="keyword">const</span> <span class="keywordtype">void</span> *<span class="keyword">const</span> out_ptr = output_accessor(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;            <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> l = 0; l &lt; output_accessor.shape().x(); ++l)</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;            {</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;                <span class="keywordtype">float</span> curr_val = <span class="keyword">reinterpret_cast&lt;</span><span class="keyword">const </span><span class="keywordtype">float</span> *<span class="keyword">&gt;</span>(out_ptr)[l];</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;                <span class="keywordflow">if</span>(curr_val &gt; val)</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;                {</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;                    max_idx = l;</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;                    val     = curr_val;</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;                }</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;            }</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;            classified_labels.push_back(max_idx);</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;        });</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;        <span class="keywordflow">return</span> classified_labels;</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    }</div><div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image&amp;#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml">arm_compute::test::Accessor</a></div><div class="ttdoc">Accessor implementation for Tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00035">Accessor.h:35</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &amp;w, L &amp;&amp;lambda_function, Ts &amp;&amp;...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div>
<div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &amp;dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00041">Window.inl:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
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<p>Initialize the network. </p>
<dl class="params"><dt>Parameters</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">batches</td><td>Number of batches. </td></tr>
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<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00060">60</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, and <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::U</a>.</p>
<div class="fragment"><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    {</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;        _batches = batches;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        <span class="comment">// Initialize input, output</span></div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;        input.allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(224U, 224U, 3U, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        output.allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(11U, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;        <span class="comment">// Initialize weights and biases</span></div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;        w_conv3x3.allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3U, 3U, 3U, 16U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        b_conv3x3.allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(16U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        depthwise_conv_block_init(0, 16, 16);</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        depthwise_conv_block_init(1, 16, 32);</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;        depthwise_conv_block_init(2, 32, 32);</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        depthwise_conv_block_init(3, 32, 64);</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        depthwise_conv_block_init(4, 64, 64);</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        depthwise_conv_block_init(5, 64, 128);</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;        depthwise_conv_block_init(6, 128, 128);</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        depthwise_conv_block_init(7, 128, 128);</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        depthwise_conv_block_init(8, 128, 128);</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        depthwise_conv_block_init(9, 128, 128);</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        depthwise_conv_block_init(10, 128, 128);</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        depthwise_conv_block_init(11, 128, 256);</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        depthwise_conv_block_init(12, 256, 256);</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;        w_conv[13].allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1U, 1U, 256U, 11U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;        b_conv[13].allocator()-&gt;init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(11U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;    }</div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&amp;#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
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<p>Runs the model. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00236">236</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>
<div class="fragment"><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    {</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;        conv3x3.run();</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;        conv3x3_act.run();</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        depthwise_conv_block_run(0);</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        depthwise_conv_block_run(1);</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        depthwise_conv_block_run(2);</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        depthwise_conv_block_run(3);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        depthwise_conv_block_run(4);</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        depthwise_conv_block_run(5);</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        depthwise_conv_block_run(6);</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        depthwise_conv_block_run(7);</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        depthwise_conv_block_run(8);</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        depthwise_conv_block_run(9);</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        depthwise_conv_block_run(10);</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        depthwise_conv_block_run(11);</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        depthwise_conv_block_run(12);</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        pool.run();</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        conv1x1[13].run();</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        logistic.run();</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        reshape.run();</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;    }</div></div><!-- fragment -->
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          <td class="memname">void sync </td>
          <td>(</td>
          <td class="paramname"></td><td>)</td>
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<p>Sync the results. </p>

<p>Definition at line <a class="el" href="_mobile_net_network_8h_source.xhtml#l00260">260</a> of file <a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a>.</p>

<p>References <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::BOUNDED_RELU</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, and <a class="el" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::U</a>.</p>
<div class="fragment"><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    {</div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        sync_if_necessary&lt;TensorType&gt;();</div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;        sync_tensor_if_necessary&lt;TensorType&gt;(output);</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    }</div></div><!-- fragment -->
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<hr/>The documentation for this class was generated from the following file:<ul>
<li>tests/networks/<a class="el" href="_mobile_net_network_8h_source.xhtml">MobileNetNetwork.h</a></li>
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